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Reena Sharma on Muck Rack

Reena Sharma

As seen in: MDPI, BioRxiv, DEV Community, Dialnet - Universidad de La Rioja, Youth Ki Awaaz, CGTLive

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Articles

Imagine if Google stopped looking for words and started understanding your thoughts. That’s what embeddings do.

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By Reena Sharma
| DEV Community
For decades, databases relied on indexes to find information quickly. Want to find every customer named “John”? The database checks an index. Need all orders placed in March? Another index. Indexes made traditional databases incredibly efficient because computers knew exactly where to look. But AI changed the rules. Users no longer search using exact words. They ask questions. They describe ideas. They expect systems to understand intent.
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The Quiet Technology Powering Almost Every AI App

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By Reena Sharma
| DEV Community
Ask someone what powers modern AI, and you’ll probably hear the same answers. “ChatGPT.” “GPT-4.” “Claude.” “Gemini.” Language models have become the face of the AI revolution. But here’s the interesting part. The smartest AI applications aren’t successful because of the model alone. They’re successful because they know where to find the right information before the model starts generating an answer.
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If your vector DB needs to see your data to search it, you’re not building private AI you’re renting confidence.

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By Reena Sharma
| DEV Community
“Private AI” has become one of the most overused phrases in modern infrastructure. Every vendor claims it. Every deck has a lock icon. Every demo promises security “by design.” But when you strip the marketing away and look at how most vector databases actually work, a hard truth emerges: If your vector database needs to decrypt your data to search it, your AI isn’t private. It’s just politely exposed.
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